Attribute Vector Guided Groupwise Registration

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Groupwise registration has been widely investigated in recent years due to its importance in analyzing population data in many clinical applications. To our best knowledge, most of the groupwise registration algorithms only utilize the intensity information. However, it is well known that using intensity only is not sufficient to achieve the anatomically sound correspondences in medical image registration. In this article, we propose a novel feature-based groupwise registration algorithm to establish the anatomical correspondence across subjects by using the attribute vector that is defined as the morphological signature for each voxel. Similar to most of the state-of-the-art groupwise registration algorithms, which simultaneously estimate the transformation fields for all subjects, we develop an energy function to minimize the intersubject discrepancies on anatomical structures and drive all subjects toward the hidden common space. To make the algorithm efficient and robust, we decouple the complex groupwise registration problem into two easy-to-solve subproblems, namely (1) robust correspondence detection and (2) dense transformation field estimation, which are systematically integrated into a unified framework. To achieve the robust correspondences in the step (1), several strategies are adopted. First, the procedure of feature matching is evaluated within a neighborhood, rather than only on a single voxel. Second, the driving voxels with distinctive image features are designed to drive the transformations of other nondriving voxels. Third, we take advantage of soft correspondence assignment not only in the spatial domain but also across the population of subjects. Specifically, multiple correspondences are allowed to alleviate the ambiguity in establishing correspondences w.r.t. a particular subject and also the contributions from different subjects are dynamically controlled throughout the registration. Eventually in the step (2), based on the correspondences established for the driving voxels, thin-plate spline is used to propagate correspondences on the driving voxels to other locations in the image. By iteratively repeating correspondence detection and dense deformation estimation, all the subjects will be aligned onto the common space. Our feature-based groupwise registration algorithm has been extensively evaluated over 18 elderly brains, 16 brains from NIREP (with 32 manually delineated labels), 40 brains from LONI LPBA40 (with 54 manually delineated labels), and 12 pairs of normal controls and simulated atrophic brain images. In all experiments, our algorithm achieves more robust and accurate registration results, compared with another groupwise algorithm and a pairwise registration method.

Section: Experiments On Loni Lpba40 Datacontrasting

confidence: 71%

Section: Experiments On Nirep Datasupporting

confidence: 50%

Feature‐based groupwise registration by hierarchical anatomical correspondence detection

Wu¹,

Wang²,

Jia³

et al. 2011

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“…To meet these criteria, we use the importance sampling strategy [Wang et al, ] to hierarchically select key points. Specifically, we smooth and normalize the gradient magnitude values over the whole image domain of subject and template, respectively.…”

Section: Methodsmentioning

confidence: 99%

S‐HAMMER: Hierarchical attribute‐guided, symmetric diffeomorphic registration for MR brain images

Kim

Wang

et al. 2013

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Deformable registration has been widely used in neuroscience studies for spatial normalization of brain images onto the standard space. Because of possible large anatomical differences across different individual brains, registration performance could be limited when trying to estimate a single directed deformation pathway, i.e., either from template to subject or from subject to template. Symmetric image registration, however, offers an effective way to simultaneously deform template and subject images toward each other until they meet at the middle point. Although some intensity-based registration algorithms have nicely incorporated this concept of symmetric deformation, the pointwise intensity matching between two images may not necessarily imply the matching of correct anatomical correspondences. Based on HAMMER registration algorithm (Shen and Davatzikos, [2002]: IEEE Trans Med Imaging 21:1421–1439), we integrate the strategies of hierarchical attribute matching and symmetric diffeomorphic deformation to build a new symmetric-diffeomorphic HAMMER registration algorithm, called as S-HAMMER. The performance of S-HAMMER has been extensively compared with 14 state-of-the-art nonrigid registration algorithms evaluated in (Klein et al., [2009]: NeuroImage 46:786–802) by using real brain images in LPBA40, IBSR18, CUMC12, and MGH10 datasets. In addition, the registration performance of S-HAMMER, by comparison with other methods, is also demonstrated on both elderly MR brain images (>70 years old) and the simulated brain images with ground-truth deformation fields. In all experiments, our proposed method achieves the best registration performance over all other registration methods, indicating the high applicability of our method in future neuroscience and clinical applications.

“…An objective function based on the pixel stack entropy is defined over all aligned images in the dataset, to solve the groupwise registration problem by a gradient‐based stochastic optimizer. The congealing registration method has been extended by Balci et al [] and Zöllei et al [] to perform non‐rigid registration, by Wang et al [] to use the attribute vector for guiding the registration and achieving more robust and accurate registration results. Another popular atlas construction by groupwise registration was proposed by Joshi et al [].…”

Section: Introductionmentioning

confidence: 99%

Intensity and sulci landmark combined brain atlas construction for Chinese pediatric population

Luo

Shi

Weng

et al. 2014

Constructing an atlas from a population of brain images is of vital importance to medical image analysis. Especially in neuroscience study, creating a brain atlas is useful for intra- and inter-population comparison. Research on brain atlas construction has attracted great attention in recent years, but the research on pediatric population is still limited, mainly due to the limited availability and the relatively low quality of pediatric magnetic resonance brain images. This article is targeted at creating a high quality representative brain atlas for Chinese pediatric population. To achieve this goal, we have designed a set of preprocessing procedures to improve the image quality and developed an intensity and sulci landmark combined groupwise registration method to align the population of images for atlas construction. As demonstrated in experiments, the newly constructed atlas can better represent the size and shape of brains of Chinese pediatric population, and show better performance in Chinese pediatric brain image analysis compared with other standard atlases.